Factors Influencing RNs’
Decisions to Work
Carol S. Brewer, Ph.D.*
Chris T. Kovner,
Kovner, Ph.D.**
William Greene, Ph.D.**
Yow WuWu-Yu, Ph.D.*
Liu Yu, Ph.D. (cand
.)*
(cand.)*
Participation PT FT
„ Why
„ Part
of larger analysis also looking at
work/notwork
work/notwork
– If the RN works, how much (PT or FT)
This work was supported by a grant from AHRQ R01
HS011320
Presented at AcademyHealth,
AcademyHealth, June 6, 2004
*University at Buffalo ** New York University
ƒ FT defined as >35 hrs per week for all jobs
Research questions
„ What
factors are associated with the work
decision (WK/NW) and amount of work
(FT/PT)?
„ Are the WK/NW and PT/FT decisions made
together or separately?
important:
– If only 10% of the PT RN population worked
FT, it would add 31,000 RNs to supply
Data sources
„ The
National Sample Survey of Registered
Nurses March 2000 (Spratley
(Spratley et al., 2001)
– County level data (some restrictions)
– Female RNs in 300 MSAs represented
„ MSA/County
level variables
– InterStudy Competitive Edge Part III Regional
Market Analysis (2002)
– Area Resource File (2002)
Sample
„ 35,358
registered nurses
„ Exclusions:
– Did not live or work in the USA
– Missing MSA codes for job and living location
– Did not work (or live) in an MSA
„ Analytic
sample was 21,123 females
Economic Environment Variables
„
Induced demand
HYP.
– Medical/surgical specialists per 1000 pop +
– Primary care practitioners per 1000 pop +
– % of HMO services paid FFS
+
„
„
-
.68
29.6%
Poverty/demand
–
–
–
ƒ Married 14, 898
ƒ Single 6,225.
1.74
0 .24
17.4%
Managed care/demand
– Index of competition
– Penetration rate of managed care
„
Means
% nonnon-HMO Medicaid as % of total MSA pop
% uninsured pop
% families living in poverty
Unemployment rate
+
?
?
7.4%
13.6%
8.1%
+
1.8%
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Demographics Characteristics
Working
Non working
Modal Age
40
- 44
50
- 54
Modal Tot Inc
$50
- 75,000
$50
- 75,000
Non
-
16.1%
12.8%
Marital status
69.8%
74.4%
Any kids < 6
18.1%
15.7%
Student
7.4%
2.9%
white
Analysis
Dominant function direct care
Staff/general duty nurses
Work in hospitals
Satisfaction (mean)
1= extremely satisfied
married
single
51.6%
50.9%
60.5%
2.31
2.42
Results
„ Analytic
method: bivariate probit
regression
– Tested hypothesis that WKNW / FTPT
decisions are related
ƒ Single RNs Rho=
Rho= -0.45, p= 0.02
ƒ Married RNs, Rho=
Rho=-0.51, p= 0.00
Significant marginal effects
PT/FT regression: Economic variables
Probability of FT
Married
decreases
Primary care physician - 0.18
ratio
Working RNs Characteristics
Single
- 0.23
Interpretation of marginal effects
Probability of working or working FT
changes (+ or - )by amount of marginal
effect at mean of variable
Ex: The probability of a 25
- 30 yr old RN
working FT decreases by 0.12 compared to a
RN < 25
Significant marginal effects
PT/FT regression: Economic variables
Probability of FT
increases
Index of competition
Married
Single
0.12
0.11
Other sig var (very small effects):
Unemployment rate, penetration rate for both
% non HMO M’caid, Specialist ratio for single only
Other sig var (very small effects): sig for both
% families in poverty
Size of MSA (small, medium, compare to large)
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Significant Demographic variables in
PartPart-time / FullFull-time regression
„
Probability of FT decreases
– All age categories: Stronger effect for married, >60
– if any children < 6
ƒ Stronger for married ((-0.30 vs -0.17)
– Baccalaureate RN vs. AD
„
Probability of FT increases
– Minorities married, ME=0.16
– Total family income,
income, (non linear) NS for married
ƒ 0.30 to 0.19 for single
– Student status NS for married
Significant organizational variables in
PT/FT regression
„ Probability
of FT decreases
– Satisfaction: small ME=- 0.01 married, ONLY
– Settings:
Settings: Educators, student health,
ambulatory care SIG vs. hospital RNs
„ Probability
of FT increases
– Function:
Function: Supervisors, teachers,
administrators vs. direct care RNs: ME=0.09
0.21 married, ONLY
– Positions:
Positions: ALL other (NP, CNS, administrator,
etc) vs. staff RNs,
RNs, Stronger for married
ƒ PT student or not a student
Implications
Conclusions
„
MSA level economic variables
– Influential on PT/FT decision, but not decision
WK/NW
„
Influence of demographic variables
„ What
to target single vs married RNs
organizations can change:
– Age, children, minority, income and student status
– Career orientation may influence PT/FT
– Education (BSN(BSN-married, Master’s single)
– Age related work conditions, esp after age 55
– Improve satisfaction
– Recruit minorities
ƒ more effect on FT work decision than WK
ƒ weak but negative = concern
„
„ Need
Organization variables
– satisfaction significant, neg,
neg, if married
– Hospital, direct care and staff RNs most likely to be
PT
– Functions and positions indicating career path more
likely to be sig
ƒ chicken or egg ? Develop career paths early
„ Work
work
decision different from how much to
Implications
„
Government policy
– Clarify education: rewards need to be clear
– Economic variables- need to understand
ƒ What can Govt manipulate?
ƒ May help in predicting regional variability in
shortages.
ƒ Job market or health of population?
– For ex: IOCIOC- perhaps hospitals are competing for nurses and
end up with more fullfull-time workers due to higher wages
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